Particle to Google BigQuery Integration
AI-first ETL from Particle into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into Google BigQuery
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Particle: IoT device fleet telemetry and events.
Google BigQuery: Serverless analytics warehouse on GCP.
How Particle entities map to Google BigQuery
| Particle entity | Google BigQuery object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| events | particle_events | TIMESTAMP events |
| variables | particle_variables | id PK · linked to particle_devices |
| firmware versions | particle_firmware_versions | id PK · linked to particle_devices |
FAQ
How does Datrise handle Particle's custom fields in Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Particle to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
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